Acknowledgement
이 논문은 2022년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구이고 (No.2022-0-00352), 2018년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구이고(No.2018R1A5A7059549), 2023년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임 (No.RS-2022-00155586, 실세계의 다양한 다운스트림 태스크를 위한 고성능 빅 하이퍼그래프 마이닝 플랫폼 개발(SW 스타랩))
References
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30-37.
- Greg Linden, Brent Smith, and Jeremy York. 2003. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing 7, 1 (2003).
- Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work. 175-186
- Hanxiong Chen, Shaoyun Shi, Yunqi Li, and Yongfeng Zhang. 2021. Neural collaborative reasoning. In Proceedings of the Web Conference 2021. 1516-1527.
- Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al. 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems. 7-10.
- Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 1725-1731.
- Lei Li, Yongfeng Zhang, and Li Chen. 2021. Personalized Transformer for Explainable Recommendation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 4947-4957
- Kyuyong Shin, Hanock Kwak, Kyung-Min Kim, Minkyu Kim, Young-Jin Park, Jisu Jeong, and Seungjae Jung. 2021. One4all User Representation for Recommender Systems in E-commerce. arXiv preprint arXiv:2106.00573 (2021).
- Kyuyong Shin, Hanock Kwak, Kyung-Min Kim, Su Young Kim, and Max Nihlen Ramstrom. 2021. Scaling Law for Recommendation Models: Towards Generalpurpose User Representations. arXiv preprint arXiv:2111.11294 (2021).
- Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research 21, 140 (2020), 1-67. http://jmlr.org/papers/v21/20-074.html